




'''simple docstring'''


import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DetaImageProcessor





class  __lowercase    (	unittest.TestCase			):







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase=7   ,				UpperCamelCase=3   ,				UpperCamelCase=30   ,				UpperCamelCase=400   ,				UpperCamelCase=True   ,				UpperCamelCase=None   ,				UpperCamelCase=True   ,				UpperCamelCase=[0.5, 0.5, 0.5]   ,				UpperCamelCase=[0.5, 0.5, 0.5]   ,				UpperCamelCase=True   ,				UpperCamelCase=1 / 255   ,				UpperCamelCase=True   ,				)					->     Any:
     # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
     __a								=       size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
     __a								=       parent
     __a								=       batch_size
     __a								=       num_channels
     __a								=       min_resolution
     __a								=       max_resolution
     __a								=       do_resize
     __a								=       size
     __a								=       do_normalize
     __a								=       image_mean
     __a								=       image_std
     __a								=       do_rescale
     __a								=       rescale_factor
     __a								=       do_pad







   def 		UpperCamelCase__      (						self   )					->     str:
     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_rescale": self.do_rescale,
         "rescale_factor": self.rescale_factor,
         "do_pad": self.do_pad,
     }







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase=False   )					->     Tuple:
     if not batched:
       __a								=       image_inputs[0]
       if isinstance(UpperCamelCase   ,				Image.Image   ):
         __a						,							__a								=       image.size
       else:
         __a						,							__a								=       image.shape[1], image.shape[2]
       if w < h:
         __a								=       int(self.size['shortest_edge'] * h / w   )
         __a								=       self.size['shortest_edge']
       elif w > h:
         __a								=       self.size['shortest_edge']
         __a								=       int(self.size['shortest_edge'] * w / h   )
       else:
         __a								=       self.size['shortest_edge']
         __a								=       self.size['shortest_edge']

     else:
       __a								=       []
       for image in image_inputs:
         __a						,							__a								=       self.get_expected_values([image]   )
         expected_values.append((expected_height, expected_width)   )
       __a								=       max(UpperCamelCase   ,				key=lambda UpperCamelCase   : item[0]   )[0]
       __a								=       max(UpperCamelCase   ,				key=lambda UpperCamelCase   : item[1]   )[1]

     return expected_height, expected_width





@require_torch
@require_vision
class  __lowercase    (	__magic_name__							,				unittest.TestCase			):
   _a    						=   DetaImageProcessor if is_vision_available() else None







   def 		UpperCamelCase__      (						self   )					->     Any:
     __a								=       DetaImageProcessingTester(self   )







   @property
   def 		UpperCamelCase__      (						self   )					->     List[str]:
     return self.image_processor_tester.prepare_image_processor_dict()







   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       self.image_processing_class(**self.image_processor_dict   )
     self.assertTrue(hasattr(UpperCamelCase   ,				'image_mean'   )   )
     self.assertTrue(hasattr(UpperCamelCase   ,				'image_std'   )   )
     self.assertTrue(hasattr(UpperCamelCase   ,				'do_normalize'   )   )
     self.assertTrue(hasattr(UpperCamelCase   ,				'do_resize'   )   )
     self.assertTrue(hasattr(UpperCamelCase   ,				'do_rescale'   )   )
     self.assertTrue(hasattr(UpperCamelCase   ,				'do_pad'   )   )
     self.assertTrue(hasattr(UpperCamelCase   ,				'size'   )   )







   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       self.image_processing_class.from_dict(self.image_processor_dict   )
     self.assertEqual(image_processor.size   ,				{'shortest_edge': 18, 'longest_edge': 1333}   )
     self.assertEqual(image_processor.do_pad   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     pass







   def 		UpperCamelCase__      (						self   )					->     str:
     # Initialize image_processing
     __a								=       self.image_processing_class(**self.image_processor_dict   )
     # create random PIL images
     __a								=       prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=UpperCamelCase   )
     for image in image_inputs:
       self.assertIsInstance(UpperCamelCase   ,				Image.Image   )

     # Test not batched input
     __a								=       image_processing(image_inputs[0]   ,				return_tensors='pt'   ).pixel_values

     __a						,							__a								=       self.image_processor_tester.get_expected_values(UpperCamelCase   )

     self.assertEqual(
         encoded_images.shape   ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,				)

     # Test batched
     __a						,							__a								=       self.image_processor_tester.get_expected_values(UpperCamelCase   ,				batched=UpperCamelCase   )

     __a								=       image_processing(UpperCamelCase   ,				return_tensors='pt'   ).pixel_values
     self.assertEqual(
         encoded_images.shape   ,				(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )   ,				)







   def 		UpperCamelCase__      (						self   )					->     Any:
     # Initialize image_processing
     __a								=       self.image_processing_class(**self.image_processor_dict   )
     # create random numpy tensors
     __a								=       prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=UpperCamelCase   ,				numpify=UpperCamelCase   )
     for image in image_inputs:
       self.assertIsInstance(UpperCamelCase   ,				np.ndarray   )

     # Test not batched input
     __a								=       image_processing(image_inputs[0]   ,				return_tensors='pt'   ).pixel_values

     __a						,							__a								=       self.image_processor_tester.get_expected_values(UpperCamelCase   )

     self.assertEqual(
         encoded_images.shape   ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,				)

     # Test batched
     __a								=       image_processing(UpperCamelCase   ,				return_tensors='pt'   ).pixel_values

     __a						,							__a								=       self.image_processor_tester.get_expected_values(UpperCamelCase   ,				batched=UpperCamelCase   )

     self.assertEqual(
         encoded_images.shape   ,				(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )   ,				)







   def 		UpperCamelCase__      (						self   )					->     str:
     # Initialize image_processing
     __a								=       self.image_processing_class(**self.image_processor_dict   )
     # create random PyTorch tensors
     __a								=       prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=UpperCamelCase   ,				torchify=UpperCamelCase   )
     for image in image_inputs:
       self.assertIsInstance(UpperCamelCase   ,				torch.Tensor   )

     # Test not batched input
     __a								=       image_processing(image_inputs[0]   ,				return_tensors='pt'   ).pixel_values

     __a						,							__a								=       self.image_processor_tester.get_expected_values(UpperCamelCase   )

     self.assertEqual(
         encoded_images.shape   ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,				)

     # Test batched
     __a								=       image_processing(UpperCamelCase   ,				return_tensors='pt'   ).pixel_values

     __a						,							__a								=       self.image_processor_tester.get_expected_values(UpperCamelCase   ,				batched=UpperCamelCase   )

     self.assertEqual(
         encoded_images.shape   ,				(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )   ,				)







   @slow
   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     # prepare image and target
     __a								=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
     with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'   ,				'r'   ) as f:
       __a								=       json.loads(f.read()   )

     __a								=       {'image_id': 3_9769, 'annotations': target}

     # encode them
     __a								=       DetaImageProcessor()
     __a								=       image_processing(images=UpperCamelCase   ,				annotations=UpperCamelCase   ,				return_tensors='pt'   )

     # verify pixel values
     __a								=       torch.Size([1, 3, 800, 1066]   )
     self.assertEqual(encoding['pixel_values'].shape   ,				UpperCamelCase   )

     __a								=       torch.tensor([0.2_796, 0.3_138, 0.3_481]   )
     self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,				UpperCamelCase   ,				atol=1e-4   )   )

     # verify area
     __a								=       torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]   )
     self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,				UpperCamelCase   )   )
     # verify boxes
     __a								=       torch.Size([6, 4]   )
     self.assertEqual(encoding['labels'][0]['boxes'].shape   ,				UpperCamelCase   )
     __a								=       torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]   )
     self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,				UpperCamelCase   ,				atol=1e-3   )   )
     # verify image_id
     __a								=       torch.tensor([3_9769]   )
     self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,				UpperCamelCase   )   )
     # verify is_crowd
     __a								=       torch.tensor([0, 0, 0, 0, 0, 0]   )
     self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,				UpperCamelCase   )   )
     # verify class_labels
     __a								=       torch.tensor([75, 75, 63, 65, 17, 17]   )
     self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,				UpperCamelCase   )   )
     # verify orig_size
     __a								=       torch.tensor([480, 640]   )
     self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,				UpperCamelCase   )   )
     # verify size
     __a								=       torch.tensor([800, 1066]   )
     self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,				UpperCamelCase   )   )







   @slow
   def 		UpperCamelCase__      (						self   )					->     int:
     # prepare image, target and masks_path
     __a								=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
     with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'   ,				'r'   ) as f:
       __a								=       json.loads(f.read()   )

     __a								=       {'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

     __a								=       pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'   )

     # encode them
     __a								=       DetaImageProcessor(format='coco_panoptic'   )
     __a								=       image_processing(images=UpperCamelCase   ,				annotations=UpperCamelCase   ,				masks_path=UpperCamelCase   ,				return_tensors='pt'   )

     # verify pixel values
     __a								=       torch.Size([1, 3, 800, 1066]   )
     self.assertEqual(encoding['pixel_values'].shape   ,				UpperCamelCase   )

     __a								=       torch.tensor([0.2_796, 0.3_138, 0.3_481]   )
     self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,				UpperCamelCase   ,				atol=1e-4   )   )

     # verify area
     __a								=       torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]   )
     self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,				UpperCamelCase   )   )
     # verify boxes
     __a								=       torch.Size([6, 4]   )
     self.assertEqual(encoding['labels'][0]['boxes'].shape   ,				UpperCamelCase   )
     __a								=       torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]   )
     self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,				UpperCamelCase   ,				atol=1e-3   )   )
     # verify image_id
     __a								=       torch.tensor([3_9769]   )
     self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,				UpperCamelCase   )   )
     # verify is_crowd
     __a								=       torch.tensor([0, 0, 0, 0, 0, 0]   )
     self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,				UpperCamelCase   )   )
     # verify class_labels
     __a								=       torch.tensor([17, 17, 63, 75, 75, 93]   )
     self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,				UpperCamelCase   )   )
     # verify masks
     __a								=       82_2873
     self.assertEqual(encoding['labels'][0]['masks'].sum().item()   ,				UpperCamelCase   )
     # verify orig_size
     __a								=       torch.tensor([480, 640]   )
     self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,				UpperCamelCase   )   )
     # verify size
     __a								=       torch.tensor([800, 1066]   )
     self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,				UpperCamelCase   )   )









'''simple docstring'''


import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()





class  __lowercase    (	__magic_name__							,				unittest.TestCase			):
   _a    						=   PriorTransformer
   _a    						=   """hidden_states"""







   @property
   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       4
     __a								=       8
     __a								=       7

     __a								=       floats_tensor((batch_size, embedding_dim)   ).to(UpperCamelCase   )

     __a								=       floats_tensor((batch_size, embedding_dim)   ).to(UpperCamelCase   )
     __a								=       floats_tensor((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

     return {
         "hidden_states": hidden_states,
         "timestep": 2,
         "proj_embedding": proj_embedding,
         "encoder_hidden_states": encoder_hidden_states,
     }







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase=0   )					->     Union[str, Any]:
     torch.manual_seed(UpperCamelCase   )
     __a								=       4
     __a								=       8
     __a								=       7

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )
     __a								=       torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

     return {
         "hidden_states": hidden_states,
         "timestep": 2,
         "proj_embedding": proj_embedding,
         "encoder_hidden_states": encoder_hidden_states,
     }







   @property
   def 		UpperCamelCase__      (						self   )					->     List[str]:
     return (4, 8)







   @property
   def 		UpperCamelCase__      (						self   )					->     str:
     return (4, 8)







   def 		UpperCamelCase__      (						self   )					->     Dict:
     __a								=       {
         'num_attention_heads': 2,
         'attention_head_dim': 4,
         'num_layers': 2,
         'embedding_dim': 8,
         'num_embeddings': 7,
         'additional_embeddings': 4,
     }
     __a								=       self.dummy_input
     return init_dict, inputs_dict







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     __a						,							__a								=       PriorTransformer.from_pretrained(
         'hf-internal-testing/prior-dummy'   ,				output_loading_info=UpperCamelCase   )
     self.assertIsNotNone(UpperCamelCase   )
     self.assertEqual(len(loading_info['missing_keys']   )   ,				0   )

     model.to(UpperCamelCase   )
     __a								=       model(**self.dummy_input   )[0]

     assert hidden_states is not None, "Make sure output is not None"







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     __a						,							__a								=       self.prepare_init_args_and_inputs_for_common()

     __a								=       self.model_class(**UpperCamelCase   )
     __a								=       inspect.signature(model.forward   )
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     __a								=       [*signature.parameters.keys()]

     __a								=       ['hidden_states', 'timestep']
     self.assertListEqual(arg_names[:2]   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'   )
     __a								=       model.to(UpperCamelCase   )

     if hasattr(UpperCamelCase   ,				'set_default_attn_processor'   ):
       model.set_default_attn_processor()

     __a								=       self.get_dummy_seed_input()

     with torch.no_grad():
       __a								=       model(**UpperCamelCase   )[0]

     __a								=       output[0, :5].flatten().cpu()
     print(UpperCamelCase   )

     # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
     # the expected output slices are not the same for CPU and GPU.
     __a								=       torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]   )
     self.assertTrue(torch_all_close(UpperCamelCase   ,				UpperCamelCase   ,				rtol=1e-2   )   )





@slow
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase=1   ,				UpperCamelCase=768   ,				UpperCamelCase=77   ,				UpperCamelCase=0   )					->     List[str]:
     torch.manual_seed(UpperCamelCase   )
     __a								=       batch_size
     __a								=       embedding_dim
     __a								=       num_embeddings

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )
     __a								=       torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

     return {
         "hidden_states": hidden_states,
         "timestep": 2,
         "proj_embedding": proj_embedding,
         "encoder_hidden_states": encoder_hidden_states,
     }







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







   @parameterized.expand(
       [
           # fmt: off
           [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
           [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
           # fmt: on
       ]   )
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Optional[Any]:
     __a								=       PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'   ,				subfolder='prior'   )
     model.to(UpperCamelCase   )
     __a								=       self.get_dummy_seed_input(seed=UpperCamelCase   )

     with torch.no_grad():
       __a								=       model(**UpperCamelCase   )[0]

     assert list(sample.shape   ) == [1, 768]

     __a								=       sample[0, :8].flatten().cpu()
     print(UpperCamelCase   )
     __a								=       torch.tensor(UpperCamelCase   )

     assert torch_all_close(UpperCamelCase   ,				UpperCamelCase   ,				atol=1e-3   )



